Using a Bottom-Up Approach to Design Computers as Metacognitive Tools to Enhance Learning of History
Bibliographic record
Abstract
A seminal study conducted by Greene, Bolick, and Robertson (2010) showed that learners do not always engage in appropriate metacognitive and self-regulatory processes while learning about history. However, little research exists to guide the design of technology-rich learning environments (TRLEs) as metacognitive tools in social sciences education. In order to address this issue, we designed a metacognitive tool using a bottom-up approach (Poitras, 2010; Poitras, Lajoie, & Hong, in prep). Thirty-two undergraduate students read an historical narrative text either with or without the benefit of the metacognitive tool. Results from process and product data suggest that learners had better recall because the metacognitive tool assisted learners to (a) notice that particular events are unexplained in the circumstances described in an historical narrative text, and (b) generate hypothetical causes to explain the occurrence of such events. We discuss the implications of these findings for the development of the MetaHistoReasoning Tool, a TRLE that assists learners’ historical reasoning while they accomplish authentic tasks of historical inquiry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".